Adaptive proportional fairness resource allocation for OFDM-based cognitive radio networks

In this paper, we study the resource allocation problem in multiuser Orthogonal Frequency Division Multiplexing (OFDM)-based cognitive radio networks. The interference introduced to Primary Users (PUs) is fully considered, as well as a set of proportional rate constraints to ensure fairness among Secondary Users (SUs). Since it is extremely computationally complex to obtain the optimal solution because of integer constraints, we adopt a two-step method to address the formulated problem. Firstly, a heuristic subchannel assignment is developed based on the normalized capacity of each OFDM subchannel by jointly considering channel gain and the interference to PUs, which approaches a rough proportional fairness and removes the intractable integer constraints. Secondly, for a given subchannel assignment, we derive a fast optimal power distribution algorithm that has a complexity of O(L ² N) by exploiting the problem’s structure, which is much lower than standard convex optimization techniques that generally have a complexity of O((N + K)³), where N, L and K are the number of subchannels, PUs and SUs, respectively. We also develop a simple power distribution algorithm with complexity of only O(L + N), while achieving above 90 % sum capacity of the upper bound. Experiments show that our proposed algorithms work quite well in practical wireless scenarios. A significant capacity gain is obtained and the proportional fairness is satisfied perfectly.     

Power optimization and subcarrier allocation for downlink MIMO-OFDMA based cognitive radio networks

Wireless Networks - This paper presents a novel resource allocation framework for downlink transmissions in MIMO-OFDMA based cognitive radio (CR) networks. In this literature, due to the...     

Joint power allocation and subcarrier pairing in OFDM-based cooperative relaying system

This paper proposes rate-maximized (MR) joint subcarrier pairing (SP) and power allocation (PA) (MR-SP&PA),a novel scheme for maximizing the weighted sum rate of the orthogonal-frequency-division multiplexing (OFDM) relaying system with a decode-and-forward (DF) relay.MR-SP&PA is based on the joint optimization of both SP and power allocation with total power constraint,and formulated as a mixed integer programming problem in the paper.The programming problem is then transformed to a convex optimization problem by using continuous relaxation,and solved in the Lagrangian dual domain.Simulation results show that MR-SP&PA can maximize the weighted sum rate under total power constraint and outperform equal power allocation (EPA) and proportion power allocation (PCG).     

Proportional-fairness resource allocation for uplink OFDM-based cognitive radio systems

This paper has proposed a proportional-fairness resource allocation algorithm, including both subcarrier assignment algorithm and power allocation algorithm, for uplink orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems. First, to get a better performance in the proposed system model, the influence factor (a,b,c) was introduced to realize the assignment of the subcarriers. Second, the transmit power of the secondary users (SUs) was allocated to the corresponding subcarriers in order to maximize the uplink capacity of the SUs subject to both power and interference constraints. With the appropriate influence factor in the subcarrier assignment, the loss of transmitted data rate arising from the fairness was minimized. Simulation results showed that the proposed algorithm can achieve a perfect fairness among the SUs while maximizing the system capacity simultaneously, and is of a low computation complexity.     

Joint power and spectrum allocation in multi-hop cognitive radio networks

Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network （CRN） with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony （ABC） algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.     

Worst subcarrier avoiding water-filling subcarrier allocation scheme for OFDM-based CRN

Efficient and reliable subcarrier power joint allocation is served as a promising problem in cognitive OFDM-based Cognitive Radio Networks (CRN). This paper focuses on optimal subcarrier allocation for OFDM-based CRN. We mainly propose subcarrier allocation scheme denoted as Worst Subcarrier Avoiding Water-filling (WSAW), which is based on Rate Adaptive (RA) criterion and three constraints are considered in CRN. The algorithm divides the assignment procedure into two phases. The first phase is an initial subcarrier allocation based on the idea of avoiding selecting the worst subcarrier in order to maximize the transmission rate; while the second phase is an iterative adjustment process which is realized by swapping pairs of subcarriers between arbitrary users. The proposed scheme could assign subcarriers in accordance with channel coherence time. Hence, real time subcarrier allocation could be implemented. Simulation results show that, comparing with the similar existing algorithms, the proposed scheme could achieve larger capacity and a near-optimal BER performance.     

Resource allocation for non-real-time services in OFDM-based cognitive radio systems

We study the resource allocation (RA) problem in a multiuser OFDM-based cognitive radio (CR) system for non-realtime (NRT) applications in which average user data rates are to be maintained proportionally. In contrast to existing algorithms designed for multiuser OFDM systems, which are unable to guarantee users proportional rates when applied in a CR system, the proposed optimal RA algorithm ensures that CR user rates are maintained in proportion to predefined target rates while at the same time providing an improved system throughput.     

Mutual interference considered power allocation in OFDM-based cognitive networks: the multiple SUs case

Power allocation for secondary users (SUs) in cognitive networks is an important issue to ensure the SUs’ quality of service. When the mutual interference between the primary users (PUs) and the SUs is taken into consideration, it is wanted to achieve the conflict-free power allocation while synchronously maximizing the capacity of the secondary network. In this paper, the optimal power allocation problem is considered in orthogonal frequency division multiplexing cognitive networks. The single SU case is primarily formulated as a constrained optimization problem. On this basis, the multiple SUs case is then studied and simulated in detail. During the analysis, the mutual interference among the PUs and the SUs is comprehensively formulated as the restrictions on the SU’s transmission power and the optimization problems are finally resolved by iterative water-filling algorithms. Consequently, the proposed power allocation scheme restrains the interference to the primary network, as well as maximizing the capacity of the secondary network. Specifying the multiple-SUs case, simulation results are exhibited in a simplified scenario to confirm the efficiency of the proposed water-filling algorithm, and the influence of the mutual interference on the power allocation and the system capacity is further illustrated.     

Opportunistic channel allocation decision making in cognitive radio communications

The global spread of wireless devices with mobile Internet access and the increasing demand of multimedia‐based applications are fueling the need for wireless broadband networks. IEEE 802.16 and 802.20 are standards for a broadband wireless access with promising cognitive radio features to support mobile Internet access. However, because of the fast changing radio environment and the demand for dynamic spectrum allocation mechanisms, these standards must continuously readjust different radio parameters. The cognitive radio makes decisions based on its built‐in inference engine, which also in time can adapt itself to different situations through the process of learning from experience. In this paper we present an automated opportunistic decision making and learning process for cognitive radio based on uncertainty reasoning algorithms. This novel approach is well suited in fast changing wireless environments with vague, incomplete, and heterogeneous information. Theory and simulations prove that decision making and learning of the cognitive radio based on the proposed approach cope with the changes in the radio environment. In this work we use fuzzy logic for the learning and decision making of the cognitive radio. Simulation also show that our approach provides accurate and precise decisions on allocating spectrum to mobile Internet users even in fast varying radio conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Dynamic wavelet-based pilot allocation algorithm for OFDM-based cognitive radio systems

In a cognitive radio system, the goal is to make better use of the radio electric spectrum, allowing non-licensed users access to those currently unused electromagnetic bands assigned to licensed users (LUs). This can be achieved using OFDM, where the non-licensed users must select the temporarily available subcarriers and turn off those subcarriers used by LUs in order to avoid interference. Hence, only a subset of the subcarriers can be used for data or pilot tone transmission. To this end, some pilot allocation algorithms have been proposed for this dynamic scenario, but they are designed in such away that an equispaced pilot placement is respected (as much as possible) while minimizing the mean squared error of the channel estimate. Nevertheless, this equispaced placement can lead to the use of an increased number of pilots in order to achieve a good channel estimation. In this work, a new pilot allocation algorithm based on wavelet transform is presented. The proposed algorithm uses the discrete wavelet transform to analyze the previous channel state information, taking the knowledge of the available subcarriers into account to provide a suboptimal solution for the pilot positions. This solution leads to a non-equispaced pilot placement, which improves the channel estimation and consequently, the system performance. Likewise, the introduced algorithm allows a reduction of the number of necessary pilots, which aids in increasing the data rate. Finally, simulation results corroborate the effectiveness of the algorithm in dynamic channel scenarios.     

Distributed joint spectrum detection and power allocation in multi-band cognitive radio networks

This article proposes a novel dynamic spectrum sharing scheme in distributed multi-band cognitive radio networks. A non-cooperative game has been utilized to model the spectrum sharing among secondary base stations (SBSs). A distributed joint spectrum detection and power allocation algorithm is designed for maximizing the downlink throughput of secondary networks. Simulation results demonstrate that the proposed algorithm converges fast and achieves a better throughput performance than uniform threshold case. Meanwhile, the convergence of algorithm is proved by Nikaido-Isoda (N-I) function method.     

OFDM认知无线电系统中多用户资源分配新算法

OFDM是认知无线电系统物理层的关键技术,本文针对多用户的OFDM认知无线电系统,提出了一种联合功率、信道和比特分配的新算法,该算法将多用户资源分配映射成多维O-1背包问题,考虑了主次用户之间的干扰,引入了次用户的带宽需求,既保证了次用户对主用户干扰功率的限制,同时又满足了各个次用户的QoS.仿真结果表明,本文的算法与...     

Robust energy efficiency power allocation for relay-assisted uplink cognitive radio networks

In this paper, we investigate a worst-case robust power allocation scheme to improve energy efficiency (EE) for an amply-and-forward relaying uplink underlay OFDM cognitive radio system with imperfect channel situation information about the channel between primary user (PU) and secondary user (SU) and the channel between SU and corresponding relay. Specifically, a max–min problem is formulated to transform the original optimization problem into maximum EE on minimum throughout channel, and an epigraph problem is introduced to obtain analytical expressions of objective power allocation. Simulation results show that the proposed EE power allocation scheme is valid and effective in EE and robustness.     

An effective suboptimal power loading scheme in OFDM-based cognitive radio systems

Orthogonal frequency division multiplexing (OFDM) is an attractive modulation candidate for Cognitive Radio (CR) networks. Effective and reliable subcarrier power allocation in OFDM-based Cognitive Radio (CR) networks is a challenging problem. This paper focuses on the power allocation for OFDM-based Cognitive Radio (CR) networks. Our objective is to maximize the total transmission rates of Secondary Users (SU) by adjusting the power of subcarrier while the interference introduced to the Primary User (PU) is within a certain range and the total power of subcarrier is not beyond the total power constraint. We investigate the optimal power allocation algorithm for OFDM-based Cognitive Radio (CR) based on convex optimization theory. Then, because of high complexity of the optimal power allocation algorithm, we propose an effective suboptimal power loading scheme. Theory analysis and simulation results show that the performance of the suboptimal power allocation algorithm is close to the performance of the optimal power allocation algorithm, while the complexity of the suboptimal power allocation algorithm is much lower.     

Low-complexity joint beamforming and power allocation for SINR balancing in cognitive radio networks

We consider the Signal-to-Interference plus Noise Ratio (SINR) balancing problem involving joint beamfoming and power allocation in the Cognitive Radio (CR) network, wherein the Single-Input Multi-Output Multiple Access Channels (SIMO-MAC) are assumed. Subject to two sets of constraints: the interference temperature constraints of Primary Users (PUs) and the peak power constraints of Cognitive Users (CUs), a low-complexity joint beamforming and power allocation algorithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection (SDMCPA-CP) for SINR balancing is proposed. Compared with the existing algorithm, the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions significantly, especially when large numbers of PUs and CUs are active, while still providing the optimal balanced SINR level for all the CUs.